Enterprise organizations have a range of core business operations able to utilize AI technologies, however, this one-size-fits-all functionality can be a non-starter for some industries. Whether due to regulation, government oversight compliance, or unique
requirements, these vertical markets may appear to be laggard adopters. Hear how these companies can innovate using intelligent solutions for sales, marketing, finance, engineering, HR, customer service, change management, corporate governance, and
more.

What will your business and industry look like in 2020? In 2025? In 2030? Join me to discuss how today’s emerging trends, including the Internet of Things, Artificial Intelligence/Machine and Deep Learning, Augmented and Virtual Reality and Blockchain,
can propel you ahead of your competition. Take a look into the future – a future that begins now – and drives innovative organizations into 2030.

2:30 AI-Driven Customer Engagement at Uber

Jeremy
Gu, Senior Data Scientist, Uber

Compared to traditional A/B testing in Marketing, continuous experiments bring the benefits of automation to marketing campaigns. After finding success with businesses such as Uber Eats, the continuous experiment approach is now taking hold in adjacent
markets, including the financial services industry. Multiple industries that have relied upon A/B testing are now turning to data insights from machine learning to implement continuous experiments.

What are the automated benefits of continuous experiments compared to traditional A/B campaign testing?

How does customer engagement change when several variations of a campaign are implemented?

What changes can Marketing anticipate in their roles as new methodologies and automation tools are deployed?

3:05 Refreshment Break in the Exhibit Hall - Last Chance for Viewing

3:40 Becoming Information-Driven in an AI World

Scott Parker, Director of Product Marketing, Sinequa

Information derived from data has emerged as a driving force in decision making and process optimization for organizations of all kinds. Today, these organizations can capture relevant information and use it to make smart, strategic, quantifiable, "information-driven"
decisions. The cognitive effort of extracting useful information from data is rather complex for any knowledge workers confronted by vast amounts of extremely diverse enterprise data. Cognitive search and analytics using NLP, ML, and deep learning
enables them to rely on information they can easily grasp and act on quickly. Attendees can expect to learn practical techniques for and the resulting value of:

Exposing patterns and relationships among disparate silos of information

Surfacing experts and expertise across the entire organization based on evidence within content

Presenting topically-relevant information from disparate sources in a unified view

Discovering what information and insights exist within your enterprise data

4:20 The Battle for AI Talent

Michelle Li, Director, MIT Master of Business Analytics Program, MIT

The battle for highly quantitative, versatile, and business-savvy talent has never been fiercer. Competency in analytics — the ability to parse large quantities of data, translate analytic insight into action and influence key business decisions
— is an essential skill needed to be successful within modern organizations. A broad array of industries — including marketing, finance, healthcare, logistics, insurance, e-commerce and manufacturing — use analytics in their business
processes. As a result, demand for graduates who can harness the power of business analytics has skyrocketed in recent years.

What are the major trends we are seeing from AI recruiters around the world?

How can enterprise organizations work with universities to create a stronger stream of future analytics talent?

What attracts graduates to opportunities, and how do companies retain top talent in the long term?